U.S. Pat. No. 8,194,964, commonly owned by the present assignee, dramatically advanced the state of the art by providing powerful technologies for identifying anatomic regions of a person delineated from image data. Likewise, commonly owned U.S. Pat. No. 8,644,579 advanced the state of the art by providing technologies for automatically identifying objects of interest from imaging scans. The present inventors have recognized that aspects of these technologies can be adapted and extended for other medical and scanning image segmentation applications.
For example, image segmentation is the process of delineating pixels into groups within an image (typically called objects) to facilitate a wide variety of machine/computer vision type tasks. For example, identifying a human face in a photograph, identifying lung nodules in a CT scan of a lung cancer patient, to identifying explosives in a CT scan of baggage at an airport.
Image data encompasses, for example, traditional two-dimensional (2D) digital photographs, satellite imagery, and ultrasound; and for three-dimensional (3D) image data include, but are not limited to, computed tomography scans such as X-ray CT, positron emission tomography (PET), and magnetic resonance imaging (MRI). In 2D a pixel represents characteristics within a region of space by characteristics such as intensity (for a black and white or greyscale image), RGB for red, green and blue color components, near infrared (NIR) for heat signatures and more. In 3D a voxel (equivalent in concept to a pixel) represents characteristics within a region of space such as density (as measured by X-ray attenuation), atomic properties captured by MRI or PET, or even simulated data obtained from scientific simulations of physical phenomena. Image data represents multidimensional characteristics of some spatially related phenomena. In the present specification, 2D and 3D data are treated the same and the term pixel is used to mean both pixel (2D) and voxel (3D).
Machine/computer vision is the task of programming a computer to identify known features or objects within image data. This task typically requires a processing step called image segmentation that groups pixels into regions that represent objects. There are often additional processing steps that follow that attempt to classify delineated objects into various groups (or classes).
Machine/computer vision and image segmentation are pervasive throughout contemporary society and can be used to analyze, process, or evaluate nearly every digital image captured. However, many opportunities for improvement in existing incarnations of these technologies remain.